Surabhi Datta
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View article: Enhancing systematic literature reviews with generative artificial intelligence: development, applications, and performance evaluation
Enhancing systematic literature reviews with generative artificial intelligence: development, applications, and performance evaluation Open
Objectives We developed and validated a large language model (LLM)-assisted system for conducting systematic literature reviews in health technology assessment (HTA) submissions. Materials and Methods We developed a five-module system usin…
View article: Patient2Trial: From patient to participant in clinical trials using large language models
Patient2Trial: From patient to participant in clinical trials using large language models Open
Purpose: Large language models (LLMs) exhibit promising language understanding and generation capabilities and have been adopted for various clinical use cases. Investigating the feasibility of leveraging LLMs in building a clinical trial …
View article: Generalized Loss-Function-Based Attacks for Object Detection Models
Generalized Loss-Function-Based Attacks for Object Detection Models Open
As artificial intelligence (AI) systems become increasingly integrated into daily life, the robustness of these systems, particularly object detection models, has gained substantial attention. Object detection is crucial in applications ra…
View article: Exploiting Watermark-Based Defense Mechanisms in Text-to-Image Diffusion Models for Unauthorized Data Usage
Exploiting Watermark-Based Defense Mechanisms in Text-to-Image Diffusion Models for Unauthorized Data Usage Open
Text-to-image diffusion models, such as Stable Diffusion, have shown exceptional potential in generating high-quality images. However, recent studies highlight concerns over the use of unauthorized data in training these models, which may …
View article: Enhancing Early Detection of Cognitive Decline in the Elderly: A Comparative Study Utilizing Large Language Models in Clinical Notes
Enhancing Early Detection of Cognitive Decline in the Elderly: A Comparative Study Utilizing Large Language Models in Clinical Notes Open
Background Large language models (LLMs) have shown promising performance in various healthcare domains, but their effectiveness in identifying specific clinical conditions in real medical records is less explored. This study evaluates LLMs…
View article: SEETrials: Leveraging Large Language Models for Safety and Efficacy Extraction in Oncology Clinical Trials
SEETrials: Leveraging Large Language Models for Safety and Efficacy Extraction in Oncology Clinical Trials Open
Background Initial insights into oncology clinical trial outcomes are often gleaned manually from conference abstracts. We aimed to develop an automated system to extract safety and efficacy information from study abstracts with high preci…
View article: AutoCriteria: a generalizable clinical trial eligibility criteria extraction system powered by large language models
AutoCriteria: a generalizable clinical trial eligibility criteria extraction system powered by large language models Open
Objectives We aim to build a generalizable information extraction system leveraging large language models to extract granular eligibility criteria information for diverse diseases from free text clinical trial protocol documents. We invest…
View article: Eye-SpatialNet: Spatial Information Extraction from Ophthalmology Notes
Eye-SpatialNet: Spatial Information Extraction from Ophthalmology Notes Open
We introduce an annotated corpus of 600 ophthalmology notes labeled with detailed spatial and contextual information of ophthalmic entities. We extend our previously proposed frame semantics-based spatial representation schema, Rad-Spatial…
View article: quEHRy: a question answering system to query electronic health records
quEHRy: a question answering system to query electronic health records Open
Objective We propose a system, quEHRy, to retrieve precise, interpretable answers to natural language questions from structured data in electronic health records (EHRs). Materials and Methods We develop/synthesize the main components of qu…
View article: Self-Supervised Learning with Radiology Reports, A Comparative Analysis of Strategies for Large Vessel Occlusion and Brain CTA Images
Self-Supervised Learning with Radiology Reports, A Comparative Analysis of Strategies for Large Vessel Occlusion and Brain CTA Images Open
Scarcity of labels for medical images is a significant barrier for training representation learning approaches based on deep neural networks. This limitation is also present when using imaging data collected during routine clinical care st…
View article: Weakly supervised spatial relation extraction from radiology reports
Weakly supervised spatial relation extraction from radiology reports Open
Objective Weak supervision holds significant promise to improve clinical natural language processing by leveraging domain resources and expertise instead of large manually annotated datasets alone. Here, our objective is to evaluate a weak…
View article: Autonomous Search and Rescue with Modeling and Simulation and Metrics
Autonomous Search and Rescue with Modeling and Simulation and Metrics Open
Unmanned Aerial Vehicles (UAVs) provide rapid exploration capabilities in search and rescue missions while accepting more risks than human operations. One limitation in that current UAVs are heavily manpower intensive and such manpower dem…
View article: Leveraging Spatial Information in Radiology Reports for Ischemic Stroke Phenotyping
Leveraging Spatial Information in Radiology Reports for Ischemic Stroke Phenotyping Open
Classifying fine-grained ischemic stroke phenotypes relies on identifying important clinical information. Radiology reports provide relevant information with context to determine such phenotype information. We focus on stroke phenotypes wi…
View article: RadLex Normalization in Radiology Reports
RadLex Normalization in Radiology Reports Open
Radiology reports have been widely used for extraction of various clinically significant information about patients' imaging studies. However, limited research has focused on standardizing the entities to a common radiology-specific vocabu…
View article: A dataset of chest X-ray reports annotated with Spatial Role Labeling annotations
A dataset of chest X-ray reports annotated with Spatial Role Labeling annotations Open
In this paper, we present a dataset consisting of 2000 chest X-ray reports (available as part of the Open-i image search platform) annotated with spatial information. The annotation is based on Spatial Role Labeling. The information includ…
View article: Rad-SpatialNet: A Frame-based Resource for Fine-Grained Spatial Relations in Radiology Reports.
Rad-SpatialNet: A Frame-based Resource for Fine-Grained Spatial Relations in Radiology Reports. Open
This paper proposes a representation framework for encoding spatial language in radiology based on frame semantics. The framework is adopted from the existing SpatialNet representation in the general domain with the aim to generate more ac…
View article: A Hybrid Deep Learning Approach for Spatial Trigger Extraction from Radiology Reports
A Hybrid Deep Learning Approach for Spatial Trigger Extraction from Radiology Reports Open
Radiology reports contain important clinical information about patients which are often tied through spatial expressions. Spatial expressions (or triggers) are mainly used to describe the positioning of radiographic findings or medical dev…
View article: Spatial Relation Extraction from Radiology Reports using Syntax-Aware Word Representations.
Spatial Relation Extraction from Radiology Reports using Syntax-Aware Word Representations. Open
In this paper, we investigate the task of spatial role labeling for extracting spatial relations from chest X-ray reports. Previous works have shown the usefulness of incorporating syntactic information in extracting spatial relations. We …
View article: Deep learning in clinical natural language processing: a methodical review
Deep learning in clinical natural language processing: a methodical review Open
Objective This article methodically reviews the literature on deep learning (DL) for natural language processing (NLP) in the clinical domain, providing quantitative analysis to answer 3 research questions concerning methods, scope, and co…